World Journal of Oncology, ISSN 1920-4531 print, 1920-454X online, Open Access
Article copyright, the authors; Journal compilation copyright, World J Oncol and Elmer Press Inc
Journal website https://wjon.elmerpub.com

Original Article

Volume 16, Number 1, February 2025, pages 30-50

Expression Profile of Thymidine Kinase Genes in Cervical Squamous Cell Carcinoma Confirmed by Various Detection Methods

Figures

↓  Figure 1. The overall design of the current study.
Figure 1.
↓  Figure 2. PRISMA flow diagram for the current study.
Figure 2.
↓  Figure 3. TK1 expression in CESC from external microarrays and RNA-seq datasets. Violin plots for: (a) GPL570; (b) GPL571; (c) GPL6244; (d) GPL96; (e) GPL4133; (f) GPL1053 and GPL1052; (g) GPL201; (h) GPL3515; (i) GPL4926; (j) GPL7025; (k) GPL16238; (l) GPL1708; (m) TCGA-GTEx. N: non-cancer controls; T: CESC samples.
Figure 3.
↓  Figure 4. The discriminatory ability of TK1 expression in distinguishing CESC from non-cancer tissues in each microarray and RNA-seq dataset. ROC curves for GPL570 (a), GPL571 (b), GPL6244 (c), GPL96 (d), GPL4133 (e), GPL1053 and GPL1052 (f), GPL201 (g), GPL3515 (h), GPL4926 (i), GPL7025 (j) GPL16238 (k), GPL1708 (l) and TCGA-GTEx datasets (m). AUC: area under curve.
Figure 4.
↓  Figure 5. TK2 expression in CESC from external microarrays and RNA-seq datasets; N: non-cancer controls; T: CESC samples.
Figure 5.
↓  Figure 6. The discriminatory ability of TK2 expression in distinguishing CESC from non-cancer tissues in each microarray and RNA-seq dataset. AUC: area under curve.
Figure 6.
↓  Figure 7. Pooled TK1 expression in CESC tissues. (a) SMD forest. (b) sROC curve. SMD: standardized mean difference; sROC: summarized receiver’s operating characteristics.
Figure 7.
↓  Figure 8. Pooled TK2 expression in CESC tissues. (a) SMD forest. (b) sROC curve. SMD: standardized mean difference; sROC: summarized receiver’s operating characteristics.
Figure 8.
↓  Figure 9. TK1 protein levels in CESC from tissue microarrays. (a) Negative staining of TK1 in non-cancer squamous epithelium tissues (× 100). (b) Negative staining of TK1 in non-cancer squamous epithelium tissues (× 200); (c) Negative staining of TK1 in non-cancer squamous epithelium tissues (× 400); (d, g) Strong staining of TK1 in CESC tissues (× 100); (e, h) Strong staining of TK1 in CESC tissues (× 200); (f, i) Strong staining of TK1 in CESC tissues (× 400); (j) Violin plots of TK1 expression in CESC and non-cancer controls; (k) ROC curves of the discriminating ability of TK1 overexpression. N: non-cancer samples; T: CESC samples; AUC: area under curve.
Figure 9.
↓  Figure 10. Perturbation effect of knocking down TK1 expression in various CESC cell lines. A lower Chronos score indicates a higher likelihood that the gene of interest is essential in a given cell line.
Figure 10.
↓  Figure 11. The prognostic significance of TK1 and TK2 expression for CESC. (a) Kaplan-Meier survival curves for overall survival of CESC patients with low or high TK1 expression. (b) Kaplan-Meier survival curves for disease-free survival of CESC patients with low or high TK1 expression. (c) Kaplan-Meier survival curves for overall survival of CESC patients with low or high TK2 expression. (d) Kaplan-Meier survival curves for disease-free survival of CESC patients with low or high TK2 expression. HR: hazard ratio.
Figure 11.
↓  Figure 12. The relationship between TK2 expression and the clinical progression of CESC. (a) TK2 expression in CESC patients with different cancer stages. (b) TK2 expression in CESC patients with different tumor histology. (c) TK2 expression in CESC patients with different status of nodal metastasis.
Figure 12.
↓  Figure 13. The TK1 and TK2 gene alterations in CESC patients. (a) The mutation type of TK1 and TK2 in CESC patients. (b) The expression of TK1 and TK2 showed a negative correlation.
Figure 13.
↓  Figure 14. The correlations between TK1 expression and the infiltration level of immune cells in CESC. Scatter plot of the correlations between TK1 expression and immune infiltration. TPM: transcripts per kilobase million.
Figure 14.
↓  Figure 15. The correlations between TK2 expression and the infiltration level of immune cells in CESC. Scatter plot of the correlations between TK2 expression and immune infiltration. TPM: transcripts per kilobase million.
Figure 15.
↓  Figure 16. The molecular docking model of targeted protein and vorinostat (a: TK1 protein; b: TK2 protein).
Figure 16.

Tables

↓  Table 1. Basic Information From All Included RNA-seq and Microarray Datasets of Cervical Cancer
 
Dataset Platform Country First author Sample type Number of tumor samples Number of non-cancer samples
GSE7803 GPL96 USA Rork Kuick Tissue 66 41
GSE9750 GPL96 USA Murty Vundavalli Tissue and cell lines
GSE46857 GPL7025 India Rita Mulherkar Tissue 25 4
GSE14404 GPL6699 India Rajkumar T Tissue 28 12
GSE29570 GPL6244 Mexico Mariano Guardado-Estrada Tissue 188 60
GSE52903 GPL6244 Mexico Ingrid Medina Martinez Tissue
GSE52904 GPL6244 Mexico Ingrid Medina Martinez Tissue
GSE89657 GPL6244 Mexico Mauricio Salcedo Vargas Tissue and cell lines
GSE39001 GPL6244 Mexico Ana Maria Espinosa Tissue
GSE27678 GPL571 United Kingdom Ian Roberts Tissue and cell lines 37 17
GSE63678 GPL571 USA Prokopios Alexandros Polyzos Tissue
GSE6791 GPL570 USA Paul Ahlquist Tissue 100 130
GSE27678 GPL570 United Kingdom Ian Roberts Tissue and cell lines
GSE63514 GPL570 USA Johan den Boon Tissue
GSE4482 GPL4926 India Chandan Kumar Tissue 3 4
GSE138080 GPL4133 Netherlands Renske DM Steenbergen Tissue 10 10
GSE4482 GPL3515 India Chandan Kumar Tissue 13 4
GSE39001 GPL201 Mexico Ana Maria Espinosa Tissue 43 12
GSE7410 GPL1708 Netherlands Petra Biewenga Tissue 40 5
GSE55940 GPL16238 China Chen Ye Tissue 5 5
GSE67522 GPL10558 United Kingdom Sweta Sharma Saha Tissue 20 22
GSE26342 GPL1053/GPL1052 USA Natalia Shulzhenko Tissue 34 20
TCGA-GTEx - - - Tissue 306 14

 

↓  Table 2. Top 10 Drugs With the Smallest Fold Changes Targeting TK1
 
Drug name P q Fold change Specificity
Amsacrine 1.93729 × 10-22 3.71892 × 10-20 -3.14512 0.000296033
Teniposide 1.81285 × 10-17 2.69123 × 10-15 -2.92398 0.000296824
Tanespimycin 2.29969 × 10-22 1.00411 × 10-19 -2.8909 0.000137912
MG-132 3.37601 × 10-14 5.36979 × 10-11 -2.82185 0.000456621
BRD-K68548958 9.27931 × 10-32 1.57734 × 10-29 -2.8178 0.000294118
SA-1919710 1.37796 × 10-13 3.83556 × 10-10 -2.80677 0.001135074
Torin-2 1.39394 × 10-17 6.33474 × 10-15 -2.80657 0.000264831
Vorinostat 6.60819 × 10-17 3.75385 × 10-15 -2.7822 0.000131492
PP-110 3.48166 × 10-29 4.78578 × 10-27 -2.77853 0.000152602

 

↓  Table 3. Top 10 Drugs With the Smallest Fold Changes Targeting TK2
 
Drug name P q Fold change Specificity
Trichostatin-a 8.01858 × 10-11 1.16704 × 10-8 2.23309 0.000340136
Trichostatin-a 7.35718 × 10-14 2.08435 × 10-12 1.65348 0.000168492
BRD-K82750814 1.72195 × 10-6 0.000106217 1.62714 0.000577701
Trichostatin-a 2.55848 × 10-6 9.05758 × 10-5 1.57637 0.000383877
Trichostatin-a 2.433 × 10-11 7.10065 × 10-10 1.47162 0.000194477
Panobinostat 3.75935 × 10-22 1.38141 × 10-20 1.46121 0.000128074
Apicidin 4.55567 × 10-19 1.0074 × 10-17 1.43291 0.000134174
Trichostatin-a 9.1091 × 10-11 1.3011 × 10-9 1.4319 0.000171028
Tozasertib 5.76929 × 10-6 0.000052139 1.41104 0.000120802
Vorinostat 1.72852 × 10-10 2.62555 × 10-9 1.38205 0.00017328